Benjamin Admin
9c33582412
Not the endpoint yet — the bigger knowledge lever first. The Advisor can say "I need 5 answers" but does not yet decide what it can find out by ITSELF. The Silent Knowledge Pass runs in front of the Advisor and, from signals existing scanners/parsers already produce (website, repository, documents, product data), deterministically derives capabilities the company demonstrably HAS + product facts that drive scope — so every recognised item shrinks the delta and removes a question. compliance/onboarding/silent_intake.py: silent_intake(signals, signal_map) -> detected_capabilities (+ evidence already in hand) + product_facts. The signal->conclusion map is curated DATA (knowledge/onboarding/intake_signal_map.yaml), signals are injected (scanners are upstream). Pure, deterministic, no LLM. advisor_start gains detected_capabilities (folded into the profile at HIGH confidence -> covered, not asked) and an auto_detected result + headline. The experience flips from a question wall to "we already recognised 4 capabilities, 2 product facts and have 4 pieces of evidence in hand — only these few remain". Order now: Silent Pass -> #58 endpoint/frontend -> #59 empirical loop. NOT new architecture, just an orchestration step in front. Non-runtime (no app caller) -> no deploy. 15 onboarding tests pass, mypy --strict clean, check-loc 0.
65 lines
1.6 KiB
Python
65 lines
1.6 KiB
Python
"""Smart Onboarding Advisor — the onboarding runtime step (orchestration over existing engines).
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Turns (company + products + certifications + target) into inferred assumptions, the next best questions
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(<=5, each self-explaining), the capability delta, top measures, evidence requests and completeness —
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with NO sales interpretation and NO regulation picking. Orchestrator only: no new engine/registry/
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meta-model; certificate->capability hypotheses and target requirements are INJECTED.
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"""
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from __future__ import annotations
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from .engine import advisor_start, apply_answer
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from .hypotheses import (
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CapabilityHypothesis,
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inferred_hypotheses,
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resolve_for_certifications,
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)
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from .observations import (
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Observation,
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ObservationType,
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empirical_confidence,
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empirical_distribution,
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reviewed,
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)
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from .silent_intake import (
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DetectedCapability,
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IntakeSignal,
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ProductFact,
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SignalMapping,
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SilentIntakeResult,
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silent_intake,
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)
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from .schemas import (
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AdvisorMeasure,
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AdvisorQuestion,
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AdvisorResult,
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InferredAssumption,
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OnboardingInput,
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RejectedAssumption,
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)
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__all__ = [
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"advisor_start",
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"apply_answer",
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"OnboardingInput",
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"AdvisorResult",
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"AdvisorQuestion",
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"AdvisorMeasure",
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"InferredAssumption",
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"RejectedAssumption",
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"CapabilityHypothesis",
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"inferred_hypotheses",
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"resolve_for_certifications",
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"Observation",
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"ObservationType",
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"empirical_distribution",
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"empirical_confidence",
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"reviewed",
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"silent_intake",
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"IntakeSignal",
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"SignalMapping",
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"DetectedCapability",
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"ProductFact",
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"SilentIntakeResult",
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]
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